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VOCVALC 2021 : VOCVALC 2021 : 4th International Workshop on Visual Odometry & Computer Vision Applications Based on Location Clues in Conjunction with CVPR | |||||||||||||||
Link: http://www.cis.rit.edu/~glpci/vocvalc2021/ | |||||||||||||||
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Call For Papers | |||||||||||||||
With the advent of autonomous driving and augmented reality, the applications of visual odometry are significantly growing. The development of smart-phones and cameras is also making the visual odometry more accessible to common users in daily life. With the increasing efforts devoted to accurately computing the position information, emerging applications based on location contexts, such as scene understanding, city navigation, and tourist recommendation, have gained significant growth. The location information can bring a rich context to facilitate a large number of challenging problems, such as landmark and traffic sign recognition under various weather and light conditions, and computer vision applications on entertainment based on location information, such as Pokemon. The motivation for the proposed workshop is soliciting scalable algorithms and systems for addressing the ever-increasing demand for accurate and real-time visual odometry, as well as the methods and applications based on the location clues. This workshop invites papers in the areas including advances in visual odometry and its applications related to computer vision in topics listed below, but not limited:
Image-based localization and navigation Monocular and stereo visual odometry Visual odometry applications on autonomous driving Augmented reality based on visual odometry Robust pose estimation solutions Multi-model visual sensor data fusion Real-time object tracking 3D scene modeling Application of deep learning on visual odometry Large-scale SLAM Map generation Scene understanding and semantic labeling Rendering and visualization of large-scale models Feature representation, indexing, storage and analysis Feature extraction and matching Object detection and recognition based on location context Landmark mining and tourism recommendation Camera calibration Video surveillance Benchmark datasets collection Papers accepted by our workshop will be published at IEEE Xplore, CVPR proceedings and CVF website. Organizers/Program chairs: Guoyu Lu, Rochester Institute of Technology Friedrich Fraundorfer, Graz University of Technology Yan Yan, Texas State University Nicu Sebe, University of Trento Chandra Kambhamettu, University of Delaware |
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